NGC1856: Using machine learning techniques to uncover detailed stellar abundances from MUSE data

TL;DR

This paper introduces a novel machine learning approach applied to MUSE data to derive detailed stellar abundances in the star cluster NGC 1856, enabling insights into chemical enrichment and galaxy evolution.

Contribution

It is the first to use data-driven machine learning on MUSE data to determine multiple stellar abundances in a star cluster.

Findings

Successfully derived [Fe/H] for 327 stars.

First to measure [Si/Fe] and [C/Fe] in NGC 1856.

Results align with existing LMC abundance data.

Abstract

We present the first application of the novel approach based on data-driven machine learning methods applied to Multi-Unit Spectroscopic Explorer (MUSE) field data to derive stellar abundances of star clusters. MUSE has been used to target more than 10,000 fields, and it is unique in its ability to study dense stellar fields such as stellar clusters providing spectra for each individual star. We use MUSE data of the extragalactic young stellar cluster NGC 1856, located in the Large Magellanic Cloud (LMC). We present the individual stellar [Fe/H] abundance of 327 cluster members in addition to [Mg/Fe], [Si/Fe], [Ti/Fe], [C/Fe], [Ni/Fe], and [Cr/Fe] abundances of subsample sets. Our results match the LMC abundances obtained in the literature for [Mg/Fe], [Ti/Fe], [Ni/Fe], and [Cr/Fe]. This study is the first to derive [Si/Fe] and [C/Fe] abundances for this cluster. The revolutionary combination of integral-field spectroscopy and data-driven modeling will allow us to understand the chemical enrichment of star clusters and their host galaxies in greater detail expanding our understanding of galaxy evolution.